Detection of concealed cars in complex cargo X-ray imagery using Deep Learning

BACKGROUND: Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators. OBJECTIVE: Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery. METHODS: X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images. RESULTS: CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected. CONCLUSIONS: We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data

The use of grain size distribution analysis of sediments and soils in forensic enquiry

The use of grain size distribution analysis in forensic enquiry was investigated with reference to four forensic case studies which contained the type of sample restraints and limitations often encountered in criminal case work. The problems of the comparison of trace and bulk samples are outlined and the need for multiple sample analysis is highlighted. It was found that the problems of soil analysis, particularly when the soil was recovered from anthropogenic sources, focused on the lack of identification of pre-, syn- and post-forensic event mixing of materials, thus obscuring the recognition of false-negative or false-positive exclusions between samples. It was found that grain size distribution analysis was a useful descriptive tool but it was concluded that if it were to be used in any other manner the derived results should be treated with great caution. The statistical analyses of these data did not improve the quality of the interpretation of the results

Automated X-ray image analysis for cargo security: Critical review and future promise

We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo

Advancing automation and robotics technology for the Space Station and for the US economy. Volume 1: Executive overview

In response to Public Law 98-371, dated July 18, 1984, the NASA Advanced Technology Advisory Committee has studied automation and robotics for use in the Space Station. The Executive Overview, Volume 1 presents the major findings of the study and recommends to NASA principles for advancing automation and robotics technologies for the benefit of the Space Station and of the U.S. economy in general. As a result of its study, the Advanced Technology Advisory Committee believes that a key element of technology for the Space Station is extensive use of advanced general-purpose automation and robotics. These systems could provide the United States with important new methods of generating and exploiting space knowledge in commercial enterprises and thereby help preserve U.S. leadership in space

Damage signature of fatigued fabric reinforced plastics in the pulsed ultrasonic polar scan

This study investigates the use of both the amplitude and time-of-flight based pulsed ultrasonic polar scan (P-UPS) for the nondestructive detection and evaluation of fatigue damage in fiber reinforced composites. Several thermoplastic carbon fabric reinforced PPS specimens (CETEX), loaded under various fatigue conditions, have been scanned at multiple material spots according to the P-UPS technique in order to extract material degradation in a quantitative way. The P-UPS results indicate that shear dominated fatigued carbon/PPS goes with a reduction of shear properties combined with large fiber distortions. The P-UPS results of the tension-tension fatigued carbon/PPS samples on the other hand reveal a directional degradation of the stiffness properties, reaching a maximum reduction of -12.8% along the loading direction. The P-UPS extracted damage characteristics are fully supported by simulations, conventional destructive tests as well as visual inspection. The results demonstrate the excellent capability of the P-UPS method for nondestructively assessing and quantifying both shear-dominated and tension-tension fatigue damage in fabric reinforced plastics

Electron Probe Micro-Analysis and Laser Microprobe Mass Analysis of Material Leached from a Limestone Cathedral

Electron probe X-ray micro-analysis (EPXMA) and Laser microprobe mass analysis (LAMMA), were applied to characterize the leachate of sandy limestones of a Belgian cathedral. Individual suspended particles, found in water that was sprayed over the cathedral walls ( 1 each ate water ), were sized and analyzed by automated EPXMA-analysis, and classified with hierarchical cluster methods. LAMMA was used to gather more information about particles, present in the solution, as well as in suspension. It was found that the leachate from black walls, had a high sulphate concentration and a large variety of particles in suspension, with different morphology and composition, with silicates as most abundant group. The leachate from white walls is characterized by a predominant Ca-rich suspension, with both original and recrystallized calcite particles, and by a much lower sulphate-ion concentration in the solution. LAMMA-analysis revealed that the organic group of the EPXMA-analysis, consists mostly of carbon-containing fly-ash particles. Hence, in general, it could be concluded that walls which are not subject to direct rainfall are generally covered with a gypsum crust, that turns black due to adhesion of soil dust and fly-ash particles, while white walls become thinner due to rainwater erosion of weathering products and original stone components

Scanning electron microscopy image representativeness: morphological data on nanoparticles.

A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca3 (PO4 )2 , and calcium hydroxyphosphate, Ca5 (PO4 )3 (OH), both naturally occurring minerals with a wide range of biomedical applications